检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

180 篇 会议
113 篇 期刊文献
2 篇 学位论文

馆藏范围

295 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

272 篇 工学
- 149 篇 计算机科学与技术...
- 103 篇 电气工程
- 42 篇 控制科学与工程
- 36 篇 信息与通信工程
- 33 篇 机械工程
- 25 篇 软件工程
- 16 篇 仪器科学与技术
- 15 篇 材料科学与工程（可...
- 10 篇 石油与天然气工程
- 8 篇 电子科学与技术（可...
- 7 篇 生物医学工程（可授...
- 6 篇 动力工程及工程热...
- 4 篇 光学工程
- 4 篇 土木工程
- 4 篇 交通运输工程
- 4 篇 环境科学与工程（可...
- 3 篇 力学（可授工学、理...
- 3 篇 水利工程
- 3 篇 测绘科学与技术
- 3 篇 化学工程与技术
57 篇 理学
- 21 篇 物理学
- 12 篇 化学
- 12 篇 生物学
- 10 篇 数学
- 6 篇 地球物理学
- 3 篇 系统科学
28 篇 管理学
- 27 篇 管理科学与工程(可...
10 篇 医学
- 9 篇 临床医学
3 篇 经济学
- 2 篇 应用经济学
2 篇 教育学
2 篇 农学
2 篇 艺术学

主题

295 篇 adaboost algorit...
48 篇 face detection
14 篇 machine learning
13 篇 haar-like featur...
10 篇 support vector m...
10 篇 feature extracti...
10 篇 feature selectio...
9 篇 pedestrian detec...
8 篇 integral image
8 篇 cascade classifi...
7 篇 face recognition
7 篇 haar-like featur...
6 篇 svm
6 篇 learning (artifi...
6 篇 genetic algorith...
6 篇 bp neural networ...
6 篇 skin color segme...
5 篇 object detection
5 篇 ensemble learnin...
5 篇 haar features

机构

3 篇 chinese acad sci...
3 篇 hexi univ inform...
2 篇 xidian univ sch ...
2 篇 southeast univ s...
2 篇 school of inform...
2 篇 hubei univ appl ...
2 篇 hunan univ coll ...
2 篇 univ chinese aca...
2 篇 beijing univ tec...
2 篇 ocean univ china...
2 篇 harbin univ sci ...
2 篇 mizoram univ aiz...
2 篇 artillery traini...
2 篇 beijing forestry...
2 篇 chinese acad sci...
2 篇 xidian univ sch ...
2 篇 nanjing univ pos...
2 篇 changan univ sch...
2 篇 dalian maritime ...
2 篇 institute of aut...

作者

3 篇 chen yong
3 篇 liao shaowen
2 篇 zhang xiangdong
2 篇 zhang lei
2 篇 teng-hui tseng
2 篇 wang jing
2 篇 zhang dandan
2 篇 song juan
2 篇 liu chong
2 篇 zhang ling
2 篇 chetri rahul lam...
2 篇 roy rajesh
2 篇 fan xiao
2 篇 li song
2 篇 anjali c.
2 篇 deb subhasish
2 篇 duan shutong
2 篇 wu xue-hua
2 篇 lin guichao
2 篇 zhang yan

语言

277 篇 英文
9 篇 其他
7 篇 中文
1 篇 法文
1 篇 土耳其文

检索条件"主题词=Adaboost algorithm"

共 295 条记录，以下是111-120 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Transfer adaboost SVM for Link Prediction in Newly Signed Social Networks using Explicit and PNR Features 19th

Transfer AdaBoost SVM for Link Prediction in Newly Signed So...

引用

19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES)

作者： Anh-Thu Nguyen-Thi Phuc Quang Nguyen Thanh Duc Ngo Tu-Anh Nguyen-Hoang Vietnam Natl Univ HCMC Univ Informat Technol Linh Trung Ward Ho Chi Minh City 700000 Vietnam

In signed social network, the user-generated content and interactions have overtaken the web. Questions of whom and what to trust has become increasingly important. We must have methods which predict the signs of links in the social network to solve this problem. We study signed social networks with positive links (friendship, fan, like, etc) and negative links (opposition, anti-fan, dislike, etc). Specifically, we focus how to effectively predict positive and negative links in newly signed social networks. With SVM model, the small amount of edge sign information in newly signed network is not adequate to train a good classifier. In this paper, we introduce an effective solution to this problem. We present a novel transfer learning framework is called Transfer adaboost with SVM (TAS) which extends boosting-based learning algorithms and incorporates properly designed RBFSVM (SVM with the RBF kernel) component classifiers. With our framework, we use explicit topological features and Positive Negative Ratio (PNR) features which are based on decision-making theory. Experimental results on three networks (Epinions, Slashdot and Wiki) demonstrate our method that can improve the prediction accuracy by 40% over baseline methods. Additionally, our method has faster performance time. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Link Prediction Signed Social Network adaboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Design and implementation of an adaboost-based Landlord bidding strategy 29

Design and implementation of an Adaboost-based Landlord bidd...

引用

第29届中国控制与决策会议

作者： Shuqin LI Meng DING Lukang PENG Shuai LIU Computer School BISTU

ISBN: (纸本)9781509046584

Landlords is a Chinese card game for all ages,each game requires at least three *** artificial intelligence service is growing rapidly to replace mankind to ensure that there are enough players online to play with people 24 hours a *** bidding is a vital part of this game,in this paper,we developed a bidding strategy by studying the real online gaming data from human's playing based on the adaboost *** choose 12 kinds of card combinations as weak classifiers,and train them with adaboost algorithm to get the final strong *** strong classifier can make an evaluation for a given hand,which can be used for making decisions whether bid or *** by different eigenvalue and card combinations data,we get an accuracy of 75%at most,basically reached the design requirements.

关键词： Landlords the bidding strategy card combination characteristics adaboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Study of Integrating adaboost and Weight Support Vector Regression Model

Study of Integrating AdaBoost and Weight Support Vector Regr...

引用

International Conference on Artificial Intelligence and Computational Intelligence

作者： Hu, Guo-Sheng Zhu, Feng-Feng Zhang, Ying-Chun Yu, Jin-Lian Shanghai Tech Inst Elect & Info Dept Comp Shanghai 201411 Peoples R China South China Univ Technol Dept Math Sci Guangzhou 510640 Peoples R China

ISBN: (纸本)9780769538167

The performance and regression precision of weak learners (accuracies should be greater than 0.5) for pattern recognition and forecasting can be upgraded using adaboost algorithm. Support vector machine (SVM) is a state-of-the-art learning machines and have been widely used in pattern recognition area since 90's of 20(th) contrary, however the performance of SVM is not stable and easily influenced due to the choice of parameters and kernel function. Hence in this paper, an integrated adaboost algorithm and weight support vector regression (WSVR) model are proposed. The proposed regression model has higher forecasting accuracy, and eliminates uncertain. The influence effects of training samples are not same, the later the training sample, more important to SVM classification ability. So we endow a different weight to each training sample. The proposed WSVR in this paper is an ideal learning machine for utilizing weighted samples, it is different from FSVM stemmed from Ref.[10]. The experimental results illustrate the proposed algorithm have better WME and WPE than single SVM algorithm and BPNN network.

关键词： adaboost algorithm weighted support vector regression electric power load forecasting error analysis

来源：评论

学校读者我要写书评

暂无评论

Customer Classification Method of Logistics Enterprises Based on BP-adaboost 3

Customer Classification Method of Logistics Enterprises Base...

引用

3rd International Conference on Applied Mathematics, Modeling and Simulation (AMMS)

作者： Shi, Xianming Li, Guangning Li, Kang Liu, Junyan Wang, Rongcai Army Engn Univ Shijiazhuang Campus Shijiazhuang Peoples R China

Effective customer classification is a key issue for logistics enterprises to carry out customer relationship management, which has been concerned and studied by relevant institutions. Based on BP and adaboost algorithm, a new customer classification method for logistics enterprises is proposed. First of all, the logistics customer value evaluation index system is designed, and the different customer characteristics are analysed. Secondly, in order to solve the problem of low accuracy of the traditional BP network, the adaboost algorithm is used to linearly combine the traditional BP network to produce a strong classifier. The simulation results show that the application of the new customer value classification method based on BP-adaboost in customer classification improves the classification accuracy.

关键词： Customer classification customer value customer relationship management adaboost algorithm BP network

来源：评论

学校读者我要写书评

暂无评论

Short-term PV power prediction model based on weather feature clustering and adaboost-GA-BP 35

Short-term PV power prediction model based on weather featur...

引用

35th Chinese Control and Decision Conference (CCDC)

作者： Liu, Yujun He, Xinrui Duan, Shutong Wang, Bo Wang, Hongqing Beijing Forestry Univ Coll Sci Beijing Peoples R China Beijing Forestry Univ Sch Econ & Management Beijing Peoples R China China Elect Power Res Inst State Key Lab Operat & Control Renewable Energy & Beijing Peoples R China

ISBN: (纸本)9798350334722

Based on the weather feature clustering, an optimized algorithm called adaboost-GA-BN is presented for solving the problem that BE neural network is easy to fall into local extremum and over-fitting in PV power short-term prediction. Initially, the K-means algorithm is applied to cluster the historical data into sunny, cloudy and overcast&rainy day. It is then the GA that optimize initial weights, forming the GA-BP neural network. The idea of adaboost algorithm is applied to train GA-BP as different weak predictors so that the adaboost-GA-BP strong predictor is combined. Finally, this paper provides a detailed performances comparison of various models. The adaboost-GA-BN predicts a 4.4% decrease in MAE and a 4.1% decrease in RMSE for the sunny test set, a 9.0% decrease in MAE and a 13.5% decrease in RMSE for the cloudy test set, and a 2.7% decrease in MAE and a 9.1% decrease in RMSE for the rainy test set, outperforming the BP model, the GA-BP model, and the adaboost-BP models. After comparative analysis, the model has better prediction accuracy and generalization ability especially for predicting cloudy weather.

关键词： photopoltaic power short-term forecast back-propagation neural network genetic algorithm adaboost algorithm weather clustering

来源：评论

学校读者我要写书评

暂无评论

Citrus Segmentation for Automatic Harvester Combined with adaboost Classifier and Leung-Malik Filter Bank

Citrus Segmentation for Automatic Harvester Combined with Ad...

引用

6th International-Federation-of-Automatic-Control (IFAC) Conference on Bio-Robotics (BIOROBOTICS)

作者： Lin, Guichao Zou, Xiangjun South China Agr Univ Guangzhou Guangdong Peoples R China

Image segmentation was the pre-processing step of fruit detection and positioning, and was challenging due to illumination change, occlusion, and cluttered background. To address segmentation problem, a novel segmentation method using adaboost classifier and texture-colour features was presented. The proposed method included two steps: (1) a fixed size sub-window was slid along every site in the image, and colour and texture features in the sub-window were extracted. Texture features were described by the Leung-Malik (LM) Filter Bank;(2) a strong classifier trained by adaboost algorithm was employed to assign every site a binary label. In order to train and evaluate the proposed method, a citrus dataset was provided which comprised 120 RGB images captured in natural illumination conditions. Twenty images in this dataset were used to train the adaboost classifier, and the remaining images were used as test set. Experiment on this test set showed that the proposed method achieved a precision of 0.867, and recall of 0.768, which confirms the validity of the proposed method. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： adaboost algorithm Leung-Malik Filter Bank naive Bayes classifier image segmentation

来源：评论

学校读者我要写书评

暂无评论

Efficient Improvement for adaboost based object detection

Efficient Improvement for Adaboost based object detection

引用

International Conference on Computational Intelligence and Natural Computing

作者： Luo Sheng Ye Xin-quan Wenzhou Univ Sch Mech & Elect Engn Wenzhou Zhejiang Prov Peoples R China

ISBN: (纸本)9780769536453

Object detection by the traditional adaboost algorithm is very time-consuming :mainly because the candidate feature number is large, and the feature number in the final strong detector is large. So this paper elevates 3 efficient optimization techniques and implements to reduce the training time. First we use some preprocessing technique to reduce the candidate features size to ten percent of the original, and then we use some implement skills to further reduce the training time. Besides these, we use double thresholds to describe each feature, which can improve the efficient of each feature, and reduce the required feature number for the final strong classifier. the experiment result show that the training of our system is hundred time faster than previous systems.

关键词： Object detection adaboost algorithm Optimization techniques Double thresholds

来源：评论

学校读者我要写书评

暂无评论

Brain image segmentation based on improved BP-adaboost neural network

Brain image segmentation based on improved BP-Adaboost neura...

引用

International Forum on Medical Imaging in Asia

作者： Chao, Zhen Kim, Dohyeon Kim, Hee-joung Yonsei Univ Dept Radiat Convergence Engn Coll Hlth Sci Wonju South Korea Yonsei Univ Dept Radiol Sci Wonju South Korea Yonsei Univ Res Inst Hlth Sci Wonju South Korea

ISBN: (数字)9781510627765

ISBN: (纸本)9781510627765

The segmentation of medical image applying in medical anatomy plays an important role in various application. So, the study of medical image processing is very important and necessary. Due to the presence of noise and complexity of structure, the existing methods have various shortcomings and the performances are not ideal. In this study, we propose a new method which based on back propagation (BP) neural network and adaboost algorithm. The BP neural network we created is 1-7-1 structure. then we trained the system by Gravitational search algorithm (Here, we use the segmented images which were obtained by classic fuzzy c-means algorithm as the ideal output data). Based on this, we established and trained 10 groups of BPNN (We also call it as weak classifier) by applying 10 groups of different data. subsequently, we adopted the adaboost algorithm to obtain the weight of each BPNN. Finally, we made up a new BPA-daboost system for image segmentation. In this experiment, we used one group of datasets: Brain MRI. A comparison with the conventional segmentation method through subjective observation and objective evaluation indexes reveals that the proposed method achieved better results based on brain image segmentation.

关键词： Back propagation neural network adaboost algorithm Gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

Improving Svm Learning Accuracy with adaboost

Improving Svm Learning Accuracy with Adaboost

引用

4th International Conference on Natural Computation (ICNC 2008)

作者： Zhang, Xiaolong Ren, Fang Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430081 Peoples R China

ISBN: (纸本)9780769533049

Support vector machine (SVM) is based on the VC theory and the principle of structural risk minimization. For some learning domains that need more accurate learning performance, SVM can be improved for this objective. This paper describes an algorithm-Boost-SVM, which puts SVM into adaboost framework to improve the learning accuracy of the SVM algorithm. By changing the weights of the training examples in the re-sampling process of adaboost, SVM appears to be more accurate. The experimental results show that the proposed method has a competitive learning ability and acquires better accuracy than SVM.

关键词： SVM Boosting algorithm adaboost algorithm

来源：评论

学校读者我要写书评

暂无评论

An Extended Set of Haar-like Features for Bird Detection Based on adaboost

An Extended Set of Haar-like Features for Bird Detection Bas...

引用

International Conference on Signal Processing, Image Processing and Pattern Recognition (SIP 2011)

作者： Huang, Chih-Cheng Tsai, Chun-Yi Yang, Horng-Chang Natl Taitung Univ Dept Comp Sci & Informat Engn Taitung Taiwan

ISBN: (纸本)9783642271823

As adaboost is an efficient method to select features, we investigate its capability in selecting effective features from a set of Haar-like features for bird detection. Moreover. adaboost is also used to construct a strong classifier for the task of detecting birds from a set of weak classifiers. We propose to add specially designed new Haar-like features to increase the detection rate of interested objects, here, namely birds. Our experiment shows that this method can increase the TPR (True Positive Rate) and decrease the FPR (False Positive Rate). In addition, a motion detection algorithm is used to detect moving objects, which are segmented from background. Using the strong classifier trained from adaboost algorithm, segmented objects are classified as bird or not. The advantage of this approach is that higher performance and real-time detection can be achieved due to the fact that the strong classifier needs not to examine all the possible sub-windows of the input image. Thus, this method can further decrease the rate of false positive.

关键词： bird detection motion detection adaboost algorithm Haar-like features

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共30页 << < 8 9 10 11 12 13 14 15 16 17 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：